Wafer imaging and processing method and apparatus

ABSTRACT

A method is disclosed whereby luminescence images are captured from as-cut or partially processed bandgap materials such as multicrystalline silicon wafers. These images are then processed to provide information about defects such as dislocations within the bandgap material. The resultant information is then utilized to predict various key parameters of a solar cell manufactured from the bandgap material, such as open circuit voltage and short circuit current. The information may also be utilized to apply a classification to the bandgap material. The methods can also be used to adjust or assess the effect of additional processing steps, such as annealing, intended to reduce the density of defects in the bandgap materials.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. application Ser.No. 12/935,654, filed Sep. 30, 2010, which is a 371 of InternationalApplication PCT/AU2009/000396 filed Mar. 31, 2009, the entirety of thecontents and subject matter of all of the above is incorporated hereinby reference.

FIELD OF THE INVENTION

The present invention relates to the field of processing luminescenceimages acquired from direct and indirect bandgap semiconductor materialssuch as silicon wafers. In particular it relates to methods andapparatus for analyzing luminescence images of wafers to obtaininformation about defects in the wafer material. This information may beused to classify the wafers or to predict operational characteristics ofdevices made from them.

BACKGROUND OF THE INVENTION

Any discussion of the prior art throughout the specification should inno way be considered as an admission that such prior art is widely knownor forms part of common general knowledge in the field.

Most commercial photovoltaic cells (in particular solar cells) today aremade from typically 10×10 cm² up to 21×21 cm² multicrystalline (mc)silicon wafers which are cut from a cast multicrystalline silicon block.The main processing steps on forming a solar cell after cutting thesilicon wafer in the most widely adopted screen printed solar cellprocess are 1) surface damage etch, 2) texturing, 3) diffusion, 4) SiNdeposition, 5) screen printing of metal contacts, 6) firing, 7) edgeisolation, and 8) electrical characterization and binning. Moresophisticated solar cell concepts use so-called selective emitterstructures in which highly doped local areas are formed under metalcontacts. Other advanced cell concepts use point contacts on the rear toimprove the rear surface recombination. Normally, the electricalperformance of a cell is measured only towards or at the end of theproduction process.

The initial wafers are normally produced by sawing a large cast siliconblock (also known as an ingot, typically up to 1×1×0.7 m³ in size) insquare (10×10 cm² up to 21/21 cm²) shaped columns (also known asbricks), which are then wire sawn into individual wafers (each typically150-300 μm thick). Currently, some wafer manufacturers use minoritycarrier lifetime measurements such as quasi steady statephotoconductance or photoconductance decay measurement along the edge ofthe square block or brick to obtain information about the local materialquality. One or several line-scans within the wafer area of individualwafers can also be used to assess the wafer quality. Normally, onlylimited two-dimensional information about lateral variations of thematerial quality within each wafer is obtained. This is as a result of ahigh volume solar cell production line typically handling 1 wafer everyone to three seconds, which limits the time available forcharacterisation.

Some individual solar cell manufacturing processes such as screenprinting and firing of electrical contacts can be performed as actualin-line processes, where the partially processed wafers are transportedthrough the process one by one, typically on a belt. Other processessuch as diffusion and SiN deposition are often carried out as batchprocesses with tens or hundreds of wafers being processedsimultaneously.

The average throughput of a typical silicon solar cell production linemay be one solar cell every one to three seconds, which limits the timeavailable for in-line characterisation of each sample. Existingspatially resolved measurements are generally too slow to yield highresolution two-dimensional information about the electronic waferquality in such short timeframes. On the other hand it is known thatsmall defects can have a large impact on device performance. Highspatial resolution (<1 mm per pixel) is thus required for a reliablecharacterisation. Manufacturers thus have limited tools that allow themto characterise the electronic properties of every wafer or of even alarge fraction of wafers going through a production process withsufficiently high lateral spatial resolution.

Certain materials that emit luminescence have a gap in their electronicdensity of states, the so-called bandgap. Such materials are referred toas bandgap materials. Direct and indirect bandgap semiconductors,including silicon, are included in this definition. Dislocations are acommon type of structural defect in semiconductors such as silicon, andtheir presence strongly affects the electronic properties of thematerials and consequently the performance of devices such as solarcells manufactured from them.

SUMMARY OF THE INVENTION

It is an object of the present invention to overcome or ameliorate atleast one of the disadvantages of the prior art, or to provide a usefulalternative.

In accordance with a first aspect, the present invention provides amethod of conducting an analysis of a bandgap material, said methodincluding the steps of:

(a) capturing a luminescence image of said bandgap material;

(b) processing said image to obtain information about defects in saidbandgap material; and

(c) utilising said information to apply a classification to said bandgapmaterial.

In a second aspect, the present invention provides method of conductingan analysis of a bandgap material, said method including the steps of:

(a) obtaining information about dislocation defects in said bandgapmaterial; and

(b) utilising said information to apply a classification to said bandgapmaterial.

In accordance with a third aspect of the present invention, there isprovided a method of predicting one or more operational characteristicsof a device fabricated from a bandgap material, said method includingthe steps of:

(a) obtaining at least one sample of said bandgap material;

(b) capturing luminescence image of said at least one sample;

(c) processing said image to obtain information about defects in saidbandgap material from said sample;

(d) analysing one or more operational characteristics of a devicefabricated from said sample; and

(e) correlating said operational characteristics with said information,wherein

-   -   (i) steps (b) and (c) are repeated for further samples of said        bandgap material to obtain further information about the defects        in said further samples; and    -   (ii) said further information is utilised to predict operational        characteristics of devices fabricated from said further samples.

Preferably, the processing of the image includes enhancing the image byany suitable technique known in the art.

The information obtained preferably includes information aboutdislocation defects of the substrate material. More preferably, theinformation includes a measure of the density of dislocation defectsacross the material.

The processing of the image may include determining an absolute orrelative area average of the dislocation density or a metric that iscorrelated with the dislocation density. The processing may also includea weighting function based on the position of the dislocation inrelation for example to the metal contacts of a solar cell. Theprocessing may also include a weighting function of the severity of adefect as apparent, for example, in its impact on the effective minoritycarrier lifetime.

The bandgap substrate material can comprise silicon. In one embodiment,the bandgap substrate material can comprise a multicrystalline siliconwafer and the step (c) can preferably include determining the likelyoperational characteristics of semiconductor devices which utilise thesilicon wafer material as their substrate. In another embodiment, thebandgap substrate material can comprise a cast monocrystalline siliconwafer.

The silicon wafer can be an as-cut unprocessed silicon wafer or apartially processed silicon wafer.

The semiconductor device can comprise a photovoltaic cell.

The image processing can comprise the step of normalising the image withrespect to the background doping concentration.

Specific defects, particularly dislocations, often appear with verysimilar spatial distribution in wafers from nearby regions in the samebrick, in other words, the spatial distribution varies very littlebetween several neighbour wafers. The embodiments can also include thestep of performing the method on a single or on multiple wafers cut fromadjacent or nearby slices of a silicon block or silicon brick andinterpolating or extrapolating the results to determine the likelyoperational characteristics of devices made on other adjacent or nearbywafers. This may allow performance predictions on a larger sample setfrom measurements on only a subset of samples.

The method can also include the step of locating low material qualityregions caused by edge defects or impurities along an edge of the wafermaterial.

The method can further include the step of: (d) utilising the results ofthe analysis step to alter parameters associated with a series ofprocessing steps in the formation of a solar cell so as to improve thequality of the solar cell. The parameters can include the conditions forfiring a metal pattern into the silicon wafer. The parameters can alsoinclude the diffusion conditions for diffusing materials into thesilicon wafer.

In certain embodiments, the parameters include the diffusion conditionsfor diffusing material in to said bandgap material, or parameters forany other process that creates doped regions in said material.

In alternative embodiments, the method can also include the step ofnormalising the photoluminescence images with respect to the backgrounddoping of the wafer. The normalising step consists of dividing theluminescence intensity in each pixel by the background dopingconcentration.

In other embodiments, the processing can include various optionsincluding weighting the area sum or area average of dislocationdensities or relative distribution of dislocation density for thelocation of said dislocation defect relative to the gridlines or metalcontacts of a voltaic cell and/or according to the severity of thedislocation defects.

The information can be obtained using photoluminescence, microwavephotoconductants decaying, optical transmission or optical reflectionmeasurements. In some cases where optical transmission or opticalreflection measurements are used, it is conducted in the 1,400 nm-1,700nm spectral range.

As discussed below, the abovementioned methods are suitable for a rangeof bandgap materials and devices but are preferably designed to predictthe operational characteristics of a photovoltaic cell including opencircuit voltage, short circuit density, fill factor or efficiency.

In accordance with a fourth aspect of the present invention, there isprovided a method of predicting one or more operational characteristicsof a device fabricated from a bandgap material, said method includingthe steps of:

(a) obtaining at least one sample of said bandgap material;

(b) obtaining information about dislocation defects in said at least onesample;

(c) analysing one or more operational characteristics of a devicefabricated from said at least one sample; and

(d) correlating said operational characteristics with said information,wherein

-   -   (i) step (b) is repeated for further samples of said bandgap        material to obtain further information about dislocation defects        in said further samples; and    -   (ii) said further information is utilised to predict said        operational characteristics of devices fabricated from said        further samples.

In accordance with a fifth aspect of the present invention, there isprovided a method of predicting one or more operational characteristicsof a device fabricated from a bandgap material, said method includingthe steps of:

(a) obtaining at least one sample of said bandgap material;

(b) obtaining information about dislocation defects in said at least onesample;

(c) utilising said information to apply a classification to said bandgapmaterial;

(d) analysing one or more operational characteristics of a devicefabricated from said at least one sample; and

(e) correlating said operational characteristics with saidclassification wherein

-   -   (i) steps (b) and (c) are repeated for further samples of said        bandgap material to obtain a further classification for each of        said further samples; and    -   (ii) said further classification is utilised to predict said        operational characteristics of the devices fabricated from said        further samples.

In the preferred embodiments the classification can also be used toreject, price and/or bin the bandgap material into different qualitycategories, or predict the operational characteristics of devicesfabricated from said bandgap material.

A sixth aspect of the present invention provides a method of conductingan analysis of a silicon wafer material, said method including the stepsof:

(a) capturing a photoluminescence image of said silicon wafer material;

(b) processing said image to obtain information about defects in saidmaterial; and

(c) utilising said information to apply a classification to said siliconwafer material.

In a seventh aspect, the present invention provides a method ofpredicting one or more operational characteristics of a devicefabricated from a silicon wafer, said method including the steps of:

(a) obtaining at least one silicon wafer sample;

(b) capturing a luminescence image of said at least one sample;

(c) processing said image to obtain information about defects in said atleast one sample;

(d) analysing one or more operational characteristics of a devicefabricated from said at least one sample; and

(e) correlating said operational characteristics with said information,wherein

-   -   (i) steps (b) and (e) are repeated for further silicon wafer        samples to obtain further information about the defects in said        further samples; and    -   (ii) said further information is utilised to predict the        operational characteristics of devices fabricated from said        further samples.

In an eighth aspect, the present invention provides a system forconducting an analysis of a bandgap material, said system including:

an image capture device for capturing a luminescence image of saidbandgap material;

an image processor for processing said image to obtain information aboutdefects in said material, and;

a classifier for utilising said information to apply a classification tosaid bandgap material.

In a ninth aspect, the present invention provides a system forconducting an analysis of a bandgap material, said system including:

an acquisition device for obtaining information about dislocationdefects in said bandgap material; and

a classifier for utilising said information to apply a classification tosaid bandgap material.

In a tenth aspect, the present invention provides a system forpredicting one or more operational characteristics of a devicefabricated from a bandgap material, said system including:

(a) an image capture device for capturing luminescence images of atleast one sample of said bandgap material;

(b) an image processor for obtaining information about defects in saidat least one sample;

(c) an analyser for analysing one or more operational characteristics ofa device fabricated from said at least one sample;

(d) a correlator for obtaining a correlation between said operationalcharacteristics and said information; and

(e) a predictor for predicting the operational characteristics ofdevices fabricated from further samples of said bandgap material, basedon said correlation and on information about said defects obtained fromsaid further samples.

In an eleventh aspect, the present invention provides a system forpredicting one or more operational characteristics of a devicefabricated from a bandgap material, said system including:

(a) an acquisition device for obtaining information about dislocationdefects in at least one sample of said bandgap material;

(b) an analyser for analysing one or more operational characteristics ofa device fabricated from said at least one sample;

(c) a correlator for obtaining a correlation between said operationalcharacteristics and said information; and

(d) a predictor for predicting the operational characteristics ofdevices fabricated from further samples of said bandgap material, basedon said correlation and on information about dislocation defectsobtained from said further samples.

In a twelfth aspect, the present invention provides a method fordistinguishing dislocation defects from grain boundaries in amulticrystalline silicon wafer, said method including the steps of:capturing a luminescence image of said silicon wafer; capturing aconventional optical image of said silicon wafer; and comparing saidluminescence and optical images.

In a thirteenth aspect, the present invention provides a system fordistinguishing dislocation defects from grain boundaries in amulticrystalline silicon wafer, said system including: a first imagecapture device for capturing a luminescence image of said silicon wafer;a second image capture device for capturing a conventional optical imageof said silicon wafer; and a comparator for comparing said luminescenceand optical images.

In a fourteenth aspect, the present invention provides a method formonitoring a process for reducing the density of defects in a bandgapmaterial, said method including the steps of:

(a) capturing a photoluminescence image of said material before saidprocess;

(b) processing said image to obtain a first measurement of the densityof said defects in said material;

(c) capturing a photoluminescence image of said material after saidprocess;

(d) processing said image to obtain a second measurement of the densityof said defects in said material; and

(e) comparing said first and second measurements.

In a fifteenth aspect, the present invention provides a method forcontrolling a process for reducing the density of defects in a bandgapmaterial, said method including the steps of:

(a) capturing one or more photoluminescence images of said materialbefore said process, or after said process, or both;

(b) processing said images to obtain one or more measurements of thedensity of said defects in said material; and

(c) adjusting one or more conditions of said process based on said oneor more measurements.

In a sixteenth aspect, the present invention provides a system formonitoring a process for reducing the density of defects in a bandgapmaterial, said system including: an image capture device for capturingphotoluminescence images of said material before and after said defectreduction process; an image processor for processing said images toobtain measurements of the density of said defects in said materialbefore and after said defect reduction process; and a comparator forcomparing said measurements.

In a seventeenth aspect, the present invention provides a system forcontrolling a process for reducing the density of defects in a bandgapmaterial, said system including: an image capture device for capturingphotoluminescence images of said material before and/or after saiddefect reduction process; an image processor for processing said imagesto obtain measurements of the density of said defects in said materialbefore and/or after said defect reduction process; and a controller foradjusting one or more conditions of said process based on at least oneof said measurements.

BRIEF DESCRIPTION OF THE DRAWINGS

A preferred embodiment of the invention will now be described, by way ofexample only, with reference to the accompanying drawings in which:

FIG. 1A illustrates the steps in a preferred embodiment;

FIG. 1B illustrates the steps in another preferred embodiment;

FIG. 2 illustrates a photoluminescence image of a multicrystallinesilicon wafer;

FIG. 3 illustrates a filtered version of the image shown in FIG. 2;

FIG. 4 illustrates a filtered photoluminescence image of a rawmulticrystalline silicon wafer;

FIG. 5 illustrates a high pass filtered photoluminescence image of afully processed solar cell made from a wafer adjacent to the wafer ofFIG. 4;

FIG. 6 illustrates a spectral Light Beam Induced Current (LBIC) map ofthe solar cell of FIG. 5;

FIG. 7 illustrates a correlation between dislocation density in rawwafers and open circuit voltage in finished cells made from neighbouringwafers;

FIG. 8 illustrates a photoluminescence image of a ‘boundary’ wafer, i.e.a wafer cut from a region close to the edge of an ingot;

FIG. 9 illustrates photoluminescence images of neighbouring wafers tothe wafer of FIG. 8; and

FIG. 10 illustrates a production line process for utilisation of thepreferred embodiment.

DESCRIPTION OF THE PREFERRED AND OTHER EMBODIMENTS

Lateral variations in the electronic material quality of the substratematerial (for example a silicon wafer) can have a large impact on keyperformance parameters of solar cells manufactured from the material,such as open circuit voltage, short circuit current density, fill factorand efficiency.

The preferred embodiment provides methods and systems that can rapidlyassess the quality of raw wafers at the start of solar cellmanufacturing or at the end of wafer manufacturing and predict cellperformance parameters or statistical variations thereof expected undernormal processing conditions. The methods and systems can also be usedto assess the effect of additional processing steps, such as annealing,intended to improve the electronic material quality.

The preferred embodiment includes analysis of photoluminescence (PL)images of a bandgap material, i.e. images of band-to-band recombinationluminescence generated by photo-excitation of the material. Inalternative embodiments the luminescence may be generated by othermeans, such as electrical excitation (electroluminescence). PL imagingis a fast and contact-less metrology technique for silicon wafercharacterisation, disclosed for example in published US patentapplication 2009/0051914 A1, the contents of which are incorporated byreference. PL imaging can be conducted on as-cut me-silicon wafers withhigh spatial resolution and a total data acquisition time of about onesecond per wafer.

The preferred embodiment has particular application to thecharacterisation of raw or partially processed silicon wafers, includingassessing the absolute or relative density distributions of defects suchas dislocations, and the subsequent prediction of solar cell parametersincluding open circuit voltage, short circuit current density, fillfactor and efficiency. The present description concentrates ondetermining the distribution density of dislocations, but the inventiveconcepts also apply to analysis of other defects that can result indegraded cell performance, including impurities, cracks and shunts.

Based on this information on the defect density distribution a wafer canbe classified or priced, with the following benefits:

-   -   1) Wafer manufacturers can use the information to assess the        quality of their outgoing material (the wafers) so that they can        demonstrate to their customers (cell manufacturers) that they        delivered wafer quality that meets the specification, or they        can price their product according to the product quality;    -   2) Cell manufacturers can use the information in the opposite        way, i.e. to check that the wafers received from the wafer        manufacturer meet the required quality specification.    -   3) Wafer manufacturers can systematically use PL images on raw        or partially processed wafers to determine the distribution of        dislocations in three dimensions (i.e. across the area of wafers        and down through the brick from which the wafers were cut). This        information may be fed back into the processing conditions for        block casting, to improve the manufacturing process.    -   4) Cell manufacturers can use wafer binning to sort wafers.        Because different optimum processing conditions exist for wafers        with variable amounts of dislocations, the wafers may then be        processed with optimised bin-specific processing conditions.        Further, variations to the processing may be achieved to effect        a higher quality outcome, e.g. a wafer may be rotated to ensure        that a specific high defect zone is not near the bus bars of an        as-completed cell. In modern cell concepts such as semiconductor        finger technology where a laser is used to define highly        laterally conductive current paths, the laser could be guided to        avoid dislocation or impurity-rich regions.    -   5) Cell manufacturers may also reject wafers with insufficient        material quality.    -   6) Cell manufacturers may also use the wafer binning to assign        wafers to specific solar cell processing lines. That assignment        can include assigning to different solar cell lines processing        the same type of solar cell but with different processing        parameters in different lines, or to different cell lines        producing different types of solar cells.    -   7) The defect distribution parameters as determined from a PL        image can become standard parameters for wafer quality.    -   8) Over time, a large database of analysis can be built up so as        to provide improved image analysis results and improved        algorithms for binning/sorting.

The steps involved in one preferred embodiment involve the capture andprocessing of photoluminescence images from bandgap materials such assilicon, and using the results of the processing to predict operationalcharacteristics of devices, such as photovoltaic (solar) cells,fabricated from the materials. The steps 1 are illustrated in FIG. 1Aand include a first step 2 of capturing a photoluminescence imagefollowed by processing 3 of the captured image, firstly to enhance theimage to highlight those defects such as dislocations, that may bepresent in the sample, and secondly to obtain information about thedistribution of those defects. This information may include an absoluteor relative area average of the defect density or a metric that iscorrelated with the defect density or with the defect distribution orboth. Finally, in step 4 the information is used to predict operationalcharacteristics of devices, such as solar cells, made from the material.

The steps involved in another preferred embodiment involve the captureand processing of photoluminescence images from bandgap materials suchas silicon, and using the results of the processing to classify thematerials. The steps 5 are illustrated in FIG. 1B and include a firststep 2 of capturing a photoluminescence image followed by processing 3of the captured image, firstly to enhance the image to highlight thosedefects such as dislocations that may be present in the sample, andsecondly to obtain information about the distribution of those defects.This information may include an absolute or relative area average of thedefect density or a metric that is correlated with the defect density orwith the defect distribution or both, and is used to assign a figure ofmerit to the sample. Finally, in step 6 this figure of merit is used toclassify the sample, e.g. for binning or pricing purposes. It will beappreciated that while operational characteristics of subsequent devicescannot be predicted with absolute accuracy, it is sufficient for binningor pricing purposes to know that solar cells made from bandgapstatistical correlations between the results of the processing 3 of thecaptured image and specific solar cell characteristics. It is alsopossible for samples to be classified on the basis of a prediction ofoperational characteristics of devices that would be made from materialwith a higher dislocation density will, on average, have inferiorproperties.

PL image capture is known and is, for example, disclosed in ‘Progresswith luminescence imaging for the characterisation of silicon wafers andsolar cells’, 22nd European Photovoltaic Solar Energy Conference,Milano, Italy, September 2007, the contents of which are herebyincorporated by cross reference.

FIG. 2 shows an example of a PL image 10 of a 1 Ωcm p-type 15×15 cm²mc-silicon wafer as would be typically used in solar cell manufacturing.This image reveals a strong variation of the material quality, i.e.bright areas 11 correspond to areas with good electronic properties anddarker areas 12 correspond to areas with poorer electronic properties.Note that in the PL imaging applications discussed here we refer tomeasurements of the band-to-band luminescence, which occurs duringradiative recombination between electrons in the conduction band andholes in the valence band of a semiconductor. In silicon the majority ofphotons emitted during that process are in the spectral range 850 nm to1300 nm at room temperature.

The PL image 10 was taken on an as-cut 15×15 cm² me-Si wafer. Furtheranalysis discloses that the image has two dominating features: 1) a longrange variation with higher luminescence intensity in the centre 11 andlower intensity towards the edges 12; and 2) short range variations 13of lower intensity. The latter patterns are interpreted as dislocationclusters. Some individual lines may also be caused byrecombination-active grain boundaries.

The next step 3 in the method of the preferred embodiment is to processthe PL image. A variety of standard image processing techniques existthat allow filtering or enhancing of specific predetermined imagefeatures, which in the present application are characteristic forexample of certain types of defects that can degrade solar cellperformance. Common types of image processing techniques, amongstothers, include noise reduction (median filtering, wavelet domainmethods, bilateral filtering, grey scale morphological reconstruction),line detection (for example using edge detection techniques such asSobel-edge detection), and image deconvolution (Weiner filtering, blinddeconvolution, iterative deconvolution via the Lucy-Richardson andLandweber method). These techniques are described in standard texts suchas ‘Digital Image Processing’ by R. Gonzalez and R. Woods (3^(rd) ed,2008).

‘High pass filtering’ is a generic term for a filtering process thatremoves low frequency components of an image or some other signal ordata set. In the present context, high pass filtering can be used toremove the influence of the long range luminescence variations from a PLimage. FIG. 3 illustrates an example result 20 of the PL image of FIG. 2image processed with a high pass filter that eliminates long rangevariations, so that the distribution of small scale variations 21,caused for example by dislocations or recombination active grainboundaries, is revealed more clearly. By way of example only, a simpleform of high pass filtering consists of applying a Fast FourierTransform (FFT) to the original image, eliminating low spatialfrequencies from the resulting frequency domain image, and applying aninverse FFT.

In alternative embodiments, further image processing steps can beapplied to the image. In one example, the image itself may be normalisedwith respect to a background doping level for improved results. Thebackground dopant may for example be boron for p-type silicon orphosphorus for n-type silicon. In another example, image contrast may beenhanced by one of the abovementioned image deconvolution techniques,taking into account a theoretical or experimentally measured pointspread function of the system. Point spread effects are generally causedby non-ideal optics, and in the case of luminescence imaging using a CCDcamera, can also be caused by lateral scattering of light within the CCDchip. In the context of dislocations, deconvolution algorithms cangreatly enhance the image contrast between dislocations or other localfeatures and the background.

Once a PL image has been enhanced, say by a combination of filtering anddeconvolution techniques, to highlight dislocations in the material, theimage processing may continue with one or more algorithms, such asSobel-edge detection, for obtaining information about the distributiondensity of the dislocations.

Distinguishing dislocations from grain boundaries in PL images ofme-silicon can be difficult in practice. Comparison of PL images withconventional optical images can be useful in this context, becauseoptical images show grain boundaries but not dislocations, allowing thetwo types of feature to be distinguished.

Specific defects, particularly dislocations, often appear with verysimilar spatial distribution in wafers from nearby regions in the samebrick, in other words, the spatial distribution varies very littlebetween several neighbour wafers. FIG. 4 shows a PL image 40 of a rawwafer after high pass filtering, FIG. 5 shows a PL image 50 of afinished cell made from a neighbouring wafer, and FIG. 6 illustrates acorresponding diffusion length image obtained from a spectral Light BeamInduced Current (LBIC) map taken on the same cell. A strong correlationis observed between the LBIC data (FIG. 6) and the PL image on theneighbouring raw wafer (FIG. 4). Hence the filtered PL image of a cellcan act as a proxy for the LBIC image, and a filtered PL image of a rawwafer can be used as an indicator of the likely operational condition ofa cell made from that wafer or from wafers cut from adjacent portions ofa brick. This allows performance predictions to be made for a largersample set, from measurements on only a subset of wafer samples.

The LBIC data in FIG. 6 representing regions with low minority carrierdiffusion length correspond to regions in which the collection ofphoto-generated carriers is relatively lower, which tends to have adirect impact on the short circuit current density of the cell. Thecorrelations found between the data of FIG. 4 and FIG. 6 suggest thatthe PL image on a raw water correlates well with the short circuitcurrent density in a finished cell. In a similar fashion, FIG. 7 shows acorrelation between the average dislocation density in raw wafers,obtained as described above from PL imaging, and the open circuitvoltage of solar cells manufactured from “sister” (neighbouring) wafers.In particular, it can be seen that lower dislocation densities correlatewith higher open circuit voltages, so that the dislocation densityobserved in raw or partially processed wafers can be used to predictcell voltage.

In a simple approach the average absolute or relative density ofdefected regions (regions with dislocations) across a PL image taken atan early stage of solar cell processing may be correlated with cellparameters such as short circuit current density, open circuit voltage,fill factor or efficiency (as shown in FIG. 7). More sophisticatedalgorithms can use weighting functions based on the position of thedefect, e.g. the proximity to the metal contacts, (e.g. grid lines orbusbars) or the edges of the cell. For example a defect that is close toa busbar or one of the metal fingers is likely to have a bigger impacton the cell voltage than a defect that is located further away from thebusbar.

The darker a defect appears in a photoluminescence image, the strongeris its recombination activity. A weighting function may also be based onthe relative intensity variation of the luminescence intensity in adefected region.

The luminescence intensity is normally proportional to the backgrounddoping, which varies in a well known way from the bottom of an ingot tothe top. Thus, if the background doping is known (either from the knownposition of the wafer in the ingot, or via a separate measurement), thenthe luminescence intensity may be normalised to the doping level,allowing a more quantitative comparison of the luminescence intensityfrom different wafers. The normalisation can involve dividing themeasured luminescence image by a constant factor that is equivalent orproportional to the average background doping concentration across thewafer, itself corresponding in a generally non-linear fashion to thewafer's position in the ingot.

The PL imaging techniques disclosed can not only be used on raw wafersand on finished cells but on wafers at any of the processing stages insolar cell production.

It is known that the density of dislocations in silicon wafers orsilicon bricks can be reduced by a thermal annealing process (K. Hartmanet al ‘Dislocation density reduction in multicrystalline silicon solarcell material by high temperature annealing’, Applied Physics Lettersvol 93(12) 122108 (2008)). On wafers such a process could be used bywafer manufacturers or by solar cell manufacturers to mitigate theimpact of dislocations on cell performance, and wafer manufacturers mayalso be able to anneal entire bricks. The annealing process could beperformed as a batch process or as a continuous in-line process, at atemperature between 1200 and 1400 degrees Celsius. In principle theprocess can be performed on any type of silicon wafer, but is mostuseful for silicon samples known to contain dislocations, i.e.multicrystalline silicon wafers (which includes both string ribbon andedge-defined film-fed growth (EFG) wafers) and cast monocrystallinewafers.

Since PL measurements enable rapid assessment of the absolute orrelative areal dislocation density and the spatial distribution ofdislocation affected areas (i.e. the absolute or relative distributionof dislocation densities), PL imaging can be used to monitor thisthermal annealing process. For example PL imaging performed on a samplebefore and after annealing would allow quantitative assessment of theefficacy of the annealing process. If individual sample tracking is notpossible, an analysis could be performed based on statistical data fordislocation densities from a number of samples, calculated from PLimages obtained before and after the annealing step.

Such process monitoring could also result in improved process control,where the annealing conditions are adjusted based on the results from PLimage analysis. This process control could be performed automatically,based on predetermined algorithms or empirical data. These annealingconditions may for example be the temperature profile (heating thesample to a desired temperature, keeping it at one or more constanttemperatures and finally cooling the sample to room temperature), or theatmosphere in the annealing furnace. Optimal process conditions forannealing may also depend on the dislocation density itself, so that theresults of a pre-anneal PL measurement may be used to determine optimalannealing conditions. This may be combined with sorting wafers intoseparate quality bins to allow processing individual bins with givenannealing conditions.

A PL image measured prior to an annealing may also be used to classifywafers into wafers with low dislocation density versus wafers with highdislocation density, allowing selection of wafers with low dislocationdensity which do not require the annealing step, thereby reducing thetotal amount of wafers that have to go through the additional annealingstep and resulting in optimised operation in wafer or cell production.

While PL imaging of band-to-band recombination luminescence is ourpreferred method for analysing the distribution density of dislocationsin silicon, it is not the only method. Under certain processing andexcitation conditions, dislocations in silicon have been demonstrated toemit light in the 1400 nm-1700 nm spectral range (I. Tarasov et al,‘Defect passivation in multicrystalline silicon for solar cells’,Applied Physics Letters vol 85(19), 4346-4348 (2004)). According to ageneralisation of Kirchhoff's law, which is applicable to luminescence,any material that has the ability to absorb light in a specificwavelength range can also emit light in that same range, and vice versa.Dislocation-rich regions in a silicon wafer should therefore absorb morestrongly in the 1400 nm-1700 nm spectral range than regions with zero orlow dislocation density. In one embodiment, reflectance or transmissionmeasurements limited to that spectral range are used to identifydislocations in silicon. These reflectance or transmission measurementscan be performed in a spatially resolved fashion using line-scan or areacameras that are sensitive in the 1400 nm-1700 nm spectral range, incombination with suitable narrow spectrum or suitably filtered lightsources.

Apart from dislocations, another example of a specific type of defectthat can be identified in starting wafer material (i.e. as-cut wafers)by luminescence imaging are impurity-related detects near the edge of awafer that result in low carrier lifetime and therefore reduced cellefficiency. As-cut multicrystalline wafers often have low materialquality around the edges or in the corners, originating from highimpurity concentrations at the bottom, top and side walls of the castmulticrystalline block from which the wafers were cut. These impuritiesoften arise from diffusion of oxygen and transition metals or othermetals from the crucible walls into the silicon block during the castingand crystallisation process. At the top of a block, the low carrierlifetime is caused by segregated impurities such as transition metalsand carbon which ‘float’ to the top during the crystallisation, whichnormally proceeds from the bottom of a block to the top. These lowcarrier lifetime regions at the bottom, top and sides of a block arenormally cut off by the wafer manufacturer before the block is choppedup into bricks, so that ideally only good quality regions are used forwafer production. Often however wafer manufacturers do not cut offenough material, so that very low carrier lifetime material is found insome wafers near an edge or near one corner.

FIG. 8 illustrates an example PL image of a wafer that was cut from theedge of a block (left). In the high impurity/low lifetime regions, whichappear generally dark in the PL image, defects such as dislocations andgrain boundaries appear brighter instead of darker. This contrastreversal occurs because of gettering of impurities by the dislocations,which improves the material quality in their vicinity. These ‘bright’dislocations appear more diffused than the ‘dark’ dislocations of lowimpurity regions, but similar line detection algorithms can still beused to highlight the dislocations and measure their areal density.

In certain embodiments, the PL imaging can thus be used to identify

1) Wafers from the bottom or top of an ingot.

2) Wafers from the edge or corners of an ingot.

Image processing of the PL images can result in the automaticidentification and classification of certain features. Classificationmay be based for example on the severity of the defect, the latterdetectable for example in PL images by the luminescence intensity, or onthe affected area, or combinations thereof. Wafer and cell manufacturersmay thus use PL imaging to sort these wafers out or bin them intovarious quality bins, and cell manufacturers can reject such wafers andsend them back to the wafer manufacturer or pay a lower price.

An example is shown in FIG. 9, where four neighbouring wafers were PLimaged (a) after surface damage etch, (b) after emitter diffusion, (c)after SiN deposition and (d) after full cell processing. The dislocationclusters that are visible in the finished cell (d) are clearly observedafter the diffusion step (b). While the dislocations are also detectableby PL on as-cut wafers, measuring after the diffusion step can beadvantageous as the PL intensity is generally enhanced after that stepallowing shorter data acquisition, lower quality equipment or resultingin higher spatial resolution images or any combination thereof. Theenhanced photoluminescence signal is a result of the field effectsurface passivation on the emitter diffused side.

In one embodiment the binning and sorting and dedicated processingprocedures may be applied after the emitter diffusion step. Further, itis possible to adjust the processing steps, such as the firingconditions, based on the results from the PL images after the diffusionstep, thereby providing improved end results.

Further modifications are possible. Wafer manufacturers could forexample use the PL imaging technique not just on wafers but on entireblocks or on the individual square columns (bricks). For example everybrick could be measured with PL imaging on one or more sides before itis sawn into wafers, allowing the wafer manufacturer to identify theposition of low lifetime regions or to detect dislocation densities onthe sides of the bricks, thereby gaining information about thedislocation density inside the ingot at a given height (i.e. at aspecific wafer position) or about the position of low lifetime regionsas caused by high impurity content. Combining several images will allowacquisition of more detailed and accurate information in that context.Wafer manufacturers may also measure the entire block to identify thebest position to chop off the impurity-rich sides, bottom and top.

When a ‘brick’ from which wafers are sliced is measured, speed is not ascrucial as for measuring individual wafers. Normally, a brick is sawninto a few hundred wafers so that there is more time available forcharacterisation. A line scanning PL tool can therefore be employedwhere the illumination is in a line shape and a linear detector array isused to capture the emission. Alternatively a series of two-dimensionalimages can be taken on various parts on the ingot or block in amapping-style arrangement, thereby generating a high resolution image ofthe entire ingot or block.

Turning to FIG. 10, there is illustrated schematically an exampleproduction line system for the manufacture of solar cells utilising themethod of the preferred embodiment. In this system wafers aretransported along a belt where they are imaged by a PL capture system.The resulting images are processed and analysed before display andstorage.

The utilisation of the preferred embodiments can be extended to bothmonocrystalline and multicrystalline wafers. Multicrystalline wafers arenormally manufactured using a casting process. Different methods existfor manufacturing monocrystalline silicon wafers, including the‘Czochralsky method’ (Cz) and the ‘float zone’ method. In terms of cellefficiencies, monocrystalline silicon is generally preferable overmulticrystalline silicon, however the higher solar cell efficiencies arenormally offset by the higher cost of producing monocrystalline wafers.Recently a method for manufacturing cast monocrystalline silicon wafershas been introduced and details of the method can be found in UnitedStates Patent Application Publication 2007/0169684 A1 in the name ofStoddard et al. A general advantage of conventional monocrystallinesilicon wafers is that they have lower impurity concentrations comparedto multicrystalline wafers and can be essentially free from structuraldefects such as dislocations or grain boundaries. In contrastdislocations can be present in the above mentioned cast monocrystallinewafers. The techniques of the preferred embodiment have equalapplication to unpassivated cast monocrystalline silicon wafers.Further, the density of structural defects as determined from a wafer byluminescence imaging at an early stage may be correlated with thecurrent density and/or voltage or other cell parameters. In comparisonwith multicrystalline wafers the identification of the structuraldefects from luminescence images is significantly easier and morereliable in the case of a cast monocrystalline silicon wafer due to theabsence of many other features commonly observed in luminescence imagestaken on multicrystalline wafers.

The preferred embodiment has direct application to other photovoltaiccell materials and can be utilised with other bandgap materials such asmonocrystalline silicon, thin film silicon, CdTe, amorphous silicon,micro-morph silicon, nanocrystalline silicon on glass, copper indiumgallium diselenide (CIGS) and related thin film materials.

Although the invention has been described with reference to specificexamples it will be appreciated by those skilled in the art that theinvention may be embodied in many other forms.

We claim:
 1. A method of classifying a plurality of silicon wafers forphotovoltaic cell manufacture, said method comprising the steps of: (a)capturing a photoluminescence image of a whole silicon wafer of each ofsaid plurality of silicon wafers for photovoltaic cell manufacture; (b)processing each of said images to obtain information about defects ineach of said plurality of silicon wafers; (c) utilizing said informationabout defects obtained from said photoluminescence image of a wholesilicon wafer to apply a classification to said whole silicon wafer foreach of said plurality of silicon wafers; and (d) using saidclassification to bin each of said silicon wafers into different qualitycategories based on likely operational characteristics of photovoltaiccells manufactured from said silicon wafers.
 2. The method according toclaim 1, wherein said processing step obtains information about defectsknown to degrade the performance of photovoltaic cells manufactured fromsaid silicon wafers.
 3. The method according to claim 2, wherein saiddefects are selected from the group consisting of dislocations,impurities, cracks and shunts.
 4. The method according to claim 2,wherein said processing step comprises applying a weighting function forthe severity of said defects according to their impact on the effectiveminority carrier lifetime of a silicon wafer.
 5. The method according toclaim 4, wherein said severity is assessed based on relative intensityvariations in said photoluminescence image.
 6. The method according toclaim 1, wherein said silicon wafers comprise as-cut or partiallyprocessed wafers.
 7. The method according to claim 6, wherein saidsilicon wafers comprise wafers after an emitter-diffusion step.
 8. Themethod according to claim 1, further comprising the step of performingthe method on one or more wafers cut from adjacent or nearby slices ofan ingot or block of silicon, and interpolating or extrapolating theresults to apply a classification to one or more neighboring wafers cutfrom said ingot or block.
 9. The method according to claim 1, furthercomprising the step of locating low material quality regions caused byedge defects or impurities along an edge of a silicon wafer.
 10. Themethod according to claim 1, further comprising the step of utilizingthe classification or likely operational characteristics to alterparameters associated with a series of processing steps in the formationof a photovoltaic cell so as to improve the quality of said photovoltaiccell.
 11. The method according to claim 10, wherein said parametersinclude the conditions for firing a metal pattern into a silicon wafer.12. The method according to claim 10, wherein said parameters includethe diffusion conditions for diffusing materials into a silicon wafer,or parameters for any other process that creates doped regions in asilicon wafer.
 13. The method according to claim 1, further comprisingthe step of normalizing each said photoluminescence image with regard tothe background doping level of each of said silicon wafers.
 14. Themethod according to claim 1, wherein said operational characteristicsinclude open circuit voltage, short circuit current density, fill factoror efficiency.
 15. The method according to claim 1, wherein theclassification of said plurality of silicon wafers includes rejecting orpricing of said silicon wafers.
 16. The method according to claim 1,further comprising the step of using the wafer binning to sort saidplurality of silicon wafers for photovoltaic cell manufacture.
 17. Asystem for classifying a plurality of silicon wafers for photovoltaiccell manufacture, said system comprising: a photoluminescence capturesystem for generating and capturing a photoluminescence image of a wholesilicon wafer of each of said plurality of silicon wafers; an imageprocessor for processing each said photoluminescence image to obtaininformation about defects in each of said plurality of silicon wafers;and a classifier for utilizing said information about defects obtainedfrom said photoluminescence image of a whole silicon wafer to apply aclassification to said whole silicon wafer for each of said plurality ofsilicon wafers and for using said classification to bin each of saidsilicon wafers into different quality categories based on likelyoperational characteristics of photovoltaic cells manufactured from saidsilicon wafers.
 18. The system according to claim 17, wherein said imageprocessor is adapted to obtain information about defects known todegrade the performance of photovoltaic cells manufactured from saidsilicon wafers.
 19. The system according to claim 18, wherein saiddefects are selected from the group consisting of dislocations,impurities, cracks and shunts.
 20. The system according to claim 19,wherein said image processor is adapted to apply a weighting functionfor the severity of said defects according to their impact on theeffective minority carrier lifetime of a silicon wafer.
 21. The systemaccording to claim 20, wherein said severity is assessed based onrelative intensity variations in said photoluminescence image.
 22. Thesystem according to claim 17, wherein said system is adapted to classifyas-cut or partially processed silicon wafers.
 23. The system accordingto claim 22, wherein said system is adapted to classify silicon wafersafter an emitter-diffusion step.
 24. The system according to claim 17,wherein said system is adapted to: obtain information about defects in,and apply a classification to, one or more silicon wafers cut fromadjacent or nearby slices of an ingot or block of silicon; and tointerpolate or extrapolate the results to apply a classification to oneor more neighboring wafers cut from said ingot or block.
 25. The systemaccording to claim 17, wherein said system is adapted to locate lowmaterial quality regions caused by edge defects or impurities along anedge of a silicon wafer.
 26. The system according to claim 17, whereinsaid image processor is adapted to normalize each said photoluminescenceimage with regard to the background doping level of each of said siliconwafers.
 27. The system according to claim 17, wherein said operationalcharacteristics include open circuit voltage, short circuit currentdensity, fill factor or efficiency.
 28. The system according to claim17, wherein said system is adapted to reject or price silicon wafersaccording to said classification.
 29. The system according to claim 17,wherein said system is adapted to sort said plurality of silicon wafersfor photovoltaic cell manufacture according to the binning intodifferent quality categories.